Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "113" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 25 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 25 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460008 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460007 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459999 | not_connected | 0.00% | 99.92% | 99.92% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0328 | 0.0318 | 0.0009 | nan | nan |
| 2459998 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.684516 | 12.861670 | 4.271412 | 4.967429 | 7.762356 | 10.007812 | 1.726179 | 0.829206 | 0.0333 | 0.0312 | 0.0012 | nan | nan |
| 2459997 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.727566 | 14.021118 | 4.534943 | 5.420502 | 7.572173 | 9.470424 | 3.633731 | 1.930536 | 0.0341 | 0.0314 | 0.0015 | nan | nan |
| 2459996 | not_connected | 100.00% | 98.92% | 98.97% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.5747 | 0.5542 | 0.4787 | nan | nan |
| 2459995 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.286895 | 15.354870 | 5.270650 | 6.141473 | 7.803975 | 9.269716 | 0.984541 | 0.312031 | 0.0383 | 0.0318 | 0.0032 | nan | nan |
| 2459994 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.742580 | 14.922684 | 4.433418 | 5.349150 | 7.660528 | 9.428578 | 2.382808 | 1.657301 | 0.0354 | 0.0312 | 0.0022 | nan | nan |
| 2459993 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.079342 | 14.098084 | 3.844613 | 4.676803 | 10.000390 | 10.758140 | 1.188097 | 1.660001 | 0.0320 | 0.0312 | 0.0008 | nan | nan |
| 2459991 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 15.068890 | 17.374787 | 4.223585 | 5.088764 | 9.027119 | 10.618328 | 0.937236 | 0.027394 | 0.0351 | 0.0312 | 0.0020 | nan | nan |
| 2459990 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.151579 | 14.319680 | 4.073097 | 4.838432 | 8.955757 | 10.917633 | 1.355256 | 0.281256 | 0.0362 | 0.0314 | 0.0026 | nan | nan |
| 2459989 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.949749 | 14.521322 | 3.603735 | 4.537270 | 7.875820 | 9.132537 | 0.640109 | -0.091004 | 0.0345 | 0.0312 | 0.0017 | nan | nan |
| 2459988 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.322824 | 16.997839 | 4.161747 | 4.912874 | 10.613453 | 13.048682 | 0.611731 | -0.034998 | 0.0342 | 0.0308 | 0.0019 | nan | nan |
| 2459987 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.854300 | 14.233273 | 4.161593 | 5.069705 | 6.283483 | 7.861719 | 1.677790 | 0.762153 | 0.0367 | 0.0313 | 0.0029 | nan | nan |
| 2459986 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.744690 | 17.428374 | 4.572095 | 5.396638 | 9.225667 | 11.106106 | 5.836201 | 9.098421 | 0.0356 | 0.0311 | 0.0023 | nan | nan |
| 2459985 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.628170 | 15.791226 | 4.275133 | 5.094373 | 7.102147 | 8.479108 | 2.020171 | 0.700819 | 0.0353 | 0.0312 | 0.0021 | nan | nan |
| 2459984 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.922209 | 15.105341 | 4.539905 | 5.375088 | 9.371786 | 12.024830 | 2.577092 | 2.146315 | 0.0373 | 0.0315 | 0.0030 | nan | nan |
| 2459983 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.675144 | 14.857114 | 4.166316 | 4.852768 | 9.145652 | 11.021211 | 3.489995 | 5.709565 | 0.0367 | 0.0315 | 0.0028 | nan | nan |
| 2459982 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.543531 | 12.021270 | 3.694240 | 4.323660 | 4.420496 | 5.190179 | 2.435728 | 3.104833 | 0.0357 | 0.0309 | 0.0026 | nan | nan |
| 2459981 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.793562 | 13.694378 | 4.268202 | 4.984151 | 10.290423 | 12.211094 | 0.975386 | 0.057199 | 0.0373 | 0.0310 | 0.0033 | nan | nan |
| 2459980 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.595290 | 13.299574 | 3.843105 | 4.658243 | 8.882755 | 10.650704 | 5.253484 | 5.073142 | 0.0372 | 0.0320 | 0.0030 | nan | nan |
| 2459979 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.030447 | 13.787157 | 3.385654 | 4.236896 | 8.831035 | 10.001747 | 2.043265 | 1.249894 | 0.0369 | 0.0316 | 0.0024 | nan | nan |
| 2459978 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.150793 | 14.024536 | 3.721949 | 4.555508 | 9.193542 | 10.817361 | 0.793353 | -0.111709 | 0.0337 | 0.0309 | 0.0017 | nan | nan |
| 2459977 | not_connected | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 4.440327 | 14.736390 | 3.264112 | 4.707433 | 3.937027 | 11.122465 | -3.003076 | 0.674563 | 0.5656 | 0.0724 | 0.4282 | nan | nan |
| 2459976 | not_connected | 100.00% | 0.00% | 99.89% | 0.00% | - | - | 4.136315 | 14.248433 | 3.638582 | 4.728117 | 3.692963 | 10.638027 | -2.236632 | 0.450745 | 0.6103 | 0.0671 | 0.4772 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 12.861670 | 10.684516 | 12.861670 | 4.271412 | 4.967429 | 7.762356 | 10.007812 | 1.726179 | 0.829206 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.021118 | 11.727566 | 14.021118 | 4.534943 | 5.420502 | 7.572173 | 9.470424 | 3.633731 | 1.930536 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 15.354870 | 13.286895 | 15.354870 | 5.270650 | 6.141473 | 7.803975 | 9.269716 | 0.984541 | 0.312031 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.922684 | 12.742580 | 14.922684 | 4.433418 | 5.349150 | 7.660528 | 9.428578 | 2.382808 | 1.657301 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.098084 | 14.079342 | 14.098084 | 3.844613 | 4.676803 | 10.000390 | 10.758140 | 1.188097 | 1.660001 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 17.374787 | 15.068890 | 17.374787 | 4.223585 | 5.088764 | 9.027119 | 10.618328 | 0.937236 | 0.027394 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.319680 | 14.319680 | 12.151579 | 4.838432 | 4.073097 | 10.917633 | 8.955757 | 0.281256 | 1.355256 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.521322 | 14.521322 | 11.949749 | 4.537270 | 3.603735 | 9.132537 | 7.875820 | -0.091004 | 0.640109 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 16.997839 | 16.997839 | 14.322824 | 4.912874 | 4.161747 | 13.048682 | 10.613453 | -0.034998 | 0.611731 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.233273 | 11.854300 | 14.233273 | 4.161593 | 5.069705 | 6.283483 | 7.861719 | 1.677790 | 0.762153 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 17.428374 | 17.428374 | 14.744690 | 5.396638 | 4.572095 | 11.106106 | 9.225667 | 9.098421 | 5.836201 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 15.791226 | 15.791226 | 13.628170 | 5.094373 | 4.275133 | 8.479108 | 7.102147 | 0.700819 | 2.020171 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 15.105341 | 12.922209 | 15.105341 | 4.539905 | 5.375088 | 9.371786 | 12.024830 | 2.577092 | 2.146315 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.857114 | 12.675144 | 14.857114 | 4.166316 | 4.852768 | 9.145652 | 11.021211 | 3.489995 | 5.709565 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 12.021270 | 10.543531 | 12.021270 | 3.694240 | 4.323660 | 4.420496 | 5.190179 | 2.435728 | 3.104833 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 13.694378 | 13.694378 | 11.793562 | 4.984151 | 4.268202 | 12.211094 | 10.290423 | 0.057199 | 0.975386 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 13.299574 | 13.299574 | 11.595290 | 4.658243 | 3.843105 | 10.650704 | 8.882755 | 5.073142 | 5.253484 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 13.787157 | 12.030447 | 13.787157 | 3.385654 | 4.236896 | 8.831035 | 10.001747 | 2.043265 | 1.249894 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.024536 | 14.024536 | 12.150793 | 4.555508 | 3.721949 | 10.817361 | 9.193542 | -0.111709 | 0.793353 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.736390 | 4.440327 | 14.736390 | 3.264112 | 4.707433 | 3.937027 | 11.122465 | -3.003076 | 0.674563 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.248433 | 14.248433 | 4.136315 | 4.728117 | 3.638582 | 10.638027 | 3.692963 | 0.450745 | -2.236632 |